Fast Generalizable Gaussian Splatting Reconstruction from Multi-View Stereo
(2405.12218)Abstract
We present MVSGaussian, a new generalizable 3D Gaussian representation approach derived from Multi-View Stereo (MVS) that can efficiently reconstruct unseen scenes. Specifically, 1) we leverage MVS to encode geometry-aware Gaussian representations and decode them into Gaussian parameters. 2) To further enhance performance, we propose a hybrid Gaussian rendering that integrates an efficient volume rendering design for novel view synthesis. 3) To support fast fine-tuning for specific scenes, we introduce a multi-view geometric consistent aggregation strategy to effectively aggregate the point clouds generated by the generalizable model, serving as the initialization for per-scene optimization. Compared with previous generalizable NeRF-based methods, which typically require minutes of fine-tuning and seconds of rendering per image, MVSGaussian achieves real-time rendering with better synthesis quality for each scene. Compared with the vanilla 3D-GS, MVSGaussian achieves better view synthesis with less training computational cost. Extensive experiments on DTU, Real Forward-facing, NeRF Synthetic, and Tanks and Temples datasets validate that MVSGaussian attains state-of-the-art performance with convincing generalizability, real-time rendering speed, and fast per-scene optimization.
Overview
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MVSGaussian leverages Multi-View Stereo to create a fast, generalizable 3D Gaussian representation that can be fine-tuned in real-time for new scenes.
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The method integrates Geometry-Aware Gaussian Representation, Hybrid Gaussian Rendering, and Multi-View Geometric Consistent Aggregation to achieve efficient and high-quality 3D reconstructions.
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MVSGaussian outperforms existing methods in terms of PSNR, SSIM, and LPIPS on various datasets and requires significantly less time for fine-tuning, making it suitable for applications in AR/VR and robotics.
Understanding MVSGaussian: Fast Real-Time 3D Reconstruction with Gaussian Splatting
Introduction
Ever wondered how we can generate high-quality 3D reconstructions from just a few images taken from different angles? This is where Novel View Synthesis (NVS) comes into play, aimed at synthesizing new views of a scene given a sparse set of images. Traditional approaches have been either too slow or required extensive computational resources. Enter MVSGaussian—a novel method leveraging Multi-View Stereo (MVS) to create a fast, generalizable 3D Gaussian representation that can be fine-tuned on the fly for new scenes.
Methodology: Breaking It Down
MVSGaussian revolves around three key components:
- Geometry-Aware Gaussian Representation: The authors harness the power of MVS to encode 3D scenes into Gaussian parameters that can be decoded efficiently.
- Hybrid Gaussian Rendering: This approach integrates volume rendering for synthesizing new views, which enhances its generalization capabilities.
- Multi-View Geometric Consistent Aggregation: Designed for quick scene-specific optimization, this strategy effectively aggregates point clouds, making it an excellent base for fine-tuning.
These innovations address several key challenges:
- Representation Generalization: Unlike static models that overfit to specific scenes, MVSGaussian's Gaussian splatting paradigm generalizes well to unseen scenes.
- Rendering Efficiency: By blending splatting and volume rendering, the method achieves real-time performance.
- Fast Fine-Tuning: A multi-view aggregation strategy offers a swift optimization process for scene-specific details.
Performance: How Does it Stack Up?
Generalization Results
MVSGaussian demonstrates robust performance across various datasets such as DTU, Real Forward-facing, NeRF Synthetic, and Tanks and Temples. For example, on the DTU test set, it tops previous methods with a PSNR of 28.21, SSIM of 0.963, and LPIPS of 0.076. Compared to other methods like ENeRF and PixelSplat, MVSGaussian offers a substantial jump in quality while maintaining real-time rendering speeds. Here's how it stacks up:
- DTU Test Set: Achieved PSNR of 28.21
- Real Forward-facing: Scored PSNR of 24.07
- NeRF Synthetic: Reached PSNR of 26.46
- Tanks and Temples: Hit PSNR of 23.28
Detailed Breakdown
To get a closer look, let's break down the performance metrics:
DTU Test Set (3-view setting):
- PSNR: 28.21
- SSIM: 0.963
- LPIPS: 0.076
Real Forward-facing:
- PSNR: 24.07
- SSIM: 0.857
- LPIPS: 0.164
NeRF Synthetic:
- PSNR: 26.46
- SSIM: 0.948
- LPIPS: 0.071
Tanks and Temples:
- PSNR: 23.28
- SSIM: 0.877
- LPIPS: 0.139
Fast Fine-Tuning: Optimization on the Fly
One standout feature of MVSGaussian is its quick fine-tuning capability. After initializing the model with a generalizable structure, the system optimizes scene-specific details within seconds. Here’s a snapshot of its optimization prowess:
- Real Forward-facing: Achieved optimal performance in just 45 seconds.
- NeRF Synthetic: Required only 50 seconds for fine-tuning.
- Tanks and Temples: Hit peak performance in 90 seconds.
These times are significantly shorter than traditional methods, where fine-tuning can take minutes to hours.
Practical and Theoretical Implications
Practical Impact
In practice, MVSGaussian can be utilized in various applications requiring fast and reliable 3D scene reconstruction, including:
- Augmented Reality (AR): Real-time scene updating as users move around.
- Virtual Reality (VR): Enhanced environments with quick scene adjustments.
- Robotics: Real-time navigation and mapping in unknown environments.
Theoretical Contributions
On a theoretical level, MVSGaussian contributes to the ongoing exploration of:
- Efficient 3D Representations: Combining MVS with Gaussian splatting pushes the boundaries on how to encode and decode 3D scenes efficiently.
- Real-Time Rendering: Hybrid rendering combining splatting and volume techniques opens new avenues for high-speed view synthesis.
Future Directions
Considering its current trajectory, future work could focus on:
- Enhancing Generalization: Further refining the model to handle more complex and diverse scenes.
- Adaptive Rendering: Developing even more nuanced rendering techniques for different applications.
- Integration: Applying MVSGaussian in conjunction with other real-time systems for more comprehensive solutions.
Conclusion
MVSGaussian sets the stage for fast, high-quality, and generalizable 3D scene reconstruction. Its combination of MVS-based representation, hybrid rendering, and quick fine-tuning provides a robust framework applicable to both academic research and industry applications. Whether you're working in AR/VR, robotics, or any field requiring dynamic 3D visualization, MVSGaussian offers a compelling solution.
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